Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction by Osahon Idemudia, Jacob Odeh Ehiorobo, Christopher Osadolor Izinyon, Idowu Ilaboya

“…From the machine learning results, random forest algorithm outperformed other methods in predicting streamflow, with a mean square error of 0.02 and a coefficient of determination of 0.98. …”
Get full text

XGBoost–random forest stacking with dual-state Kalman filtering for real-time battery SOC estimation by Robin K.E. Tau, Abid Yahya, Mmoloki Mangwala, Nonofo M.J. Ditshego

“…HEAD-KF yields a global mean-absolute error of Image 5 SOC, keeps dynamic-discharge error to Image 6, and updates in Image 7 while consuming Image 8 per prediction. …”
Get full text

Explainable Machine Learning to Predict the Construction Cost of Power Plant Based on Random Forest and Shapley Method by Suha Falih Mahdi Alazawy, Mohammed Ali Ahmed, Saja Hadi Raheem, Hamza Imran, Luís Filipe Almeida Bernardo, Hugo Alexandre Silva Pinto

“…This investigation employed the Random Forest (RF) algorithm to estimate the overall construction cost of a power plant. …”
Get full text

Random Ensemble MARS: Model Selection in Multivariate Adaptive Regression Splines Using Random Forest Approach by Mehmet Ali Cengiz, Dilek Sabancı

“…This study presented REMARS (Random Ensemble MARS), a new MARS model selection approach obtained using the Random Forest (RF) algorithm. 200 training and test data set generated via the Bagging method were analysed in the MARS analysis engine. …”
Get full text

SP-RF-ARIMA: A sparse random forest and ARIMA hybrid model for electric load forecasting by Kamran Hassanpouri Baesmat, Farhad Shokoohi, Zeinab Farrokhi

“…This methodology, termed SP-RF-ARIMA, is evaluated against existing approaches; it demonstrates more than 40% reduction in mean absolute error and root mean square error compared to the second-best method.…”
Get full text

Spatiotemporal Bayes model for estimating the number of hotspots as an indicator of forest and land fires in Kalimantan Island, Indonesia by FADILLAH ROHIMAHASTUTI, ANIK DJURAIDAH, HARI WIJAYANTO

“… Forest and land fires often occur on the island of Kalimantan and have a widespread impact on neighboring countries. …”
Get full text

Joint-Pixel Inversion for Ground Phase and Forest Height Estimation Using Spaceborne Polarimetric SAR Interferometry by Zenghui Huang, Jingyu Gao, Xiaolei Lv, Xiaoshuai Li

“…Quantitative evaluations in forest height estimation show that the proposed method achieves a lower mean error (1.23 m) and RMSE (3.67 m) than the existing method (mean error: 3.09 m; RMSE: 4.70 m), demonstrating its improved reliability.…”
Get full text

An Intelligent Crow Search Optimization and Bi-GRU for Forest Fire Detection System Using Internet of Things by Syed Abdul Moeed, Bellam Surendra Babu, M. Sreevani, B. V. Devendra Rao, R. Raja Kumar and Gouse Baig Mohammed

“…The high accuracy and low error rate underscore the potential of this system to be a valuable tool in mitigating the risks associated with forest fires, ultimately contributing to the preservation of natural ecosystems.…”
Get full text

Study on rapid determination method of ash content in wheat flour based on stochastic forest regression model by LIU Yanqun, XIAO Fugang, CHEN Caihong

“…ObjectiveTo achieve rapid and accurate determination of ash content in wheat flour.MethodsBy preprocessing wheat raw materials and analyzing key influencing factors such as milling time and conductivity in depth, these factors were introduced as characteristic variables into a random forest regression model to construct a wheat flour ash content determination model. …”
Get full text

Integration of UAV Multispectral Remote Sensing and Random Forest for Full-Growth Stage Monitoring of Wheat Dynamics by Donghui Zhang, Hao Qi, Xiaorui Guo, Haifang Sun, Jianan Min, Si Li, Liang Hou, Liangjie Lv

“…The results demonstrated that the NDRE and TVI indices were the most effective indices for monitoring wheat growth. The random forest model exhibited superior predictive accuracy, with a mean squared error (MSE) significantly lower than that of traditional regression models, particularly during the flowering and ripening stages, where the prediction error for plant height was less than 1.01 cm. …”
Get full text

Modeling of CO₂ Efflux from Forest and Grassland Soils Depending on Weather Conditions by Sergey Kivalov, Irina Kurganova, Sergey Bykhovets, Dmitriy Khoroshaev, Valentin Lopes de Gerenyu, Yiping Wu, Tatiana Myakshina, Yakov Kuzyakov, Irina Priputina

“…The experimental data from 25 years of field observations were utilized to identify the optimal site- and weather-specific models, parameterized for normal, wet, and dry years, for the forest and grassland ecosystems located on similar Entic Podzols (Arenic) in the same bioclimatic coniferous–deciduous forest zone. …”
Get full text

Tropical Forest Carbon Accounting Through Deep Learning-Based Species Mapping and Tree Crown Delineation by Georgia Ray, Minerva Singh

“…Tropical forests are essential ecosystems recognized for their carbon sequestration and biodiversity benefits. …”
Get full text

Random-forest-based task pricing model and task-accomplished model for crowdsourced emergency information acquisition by Wenxiang Li, Shengqun Chen, Lijin Lin, Li Chen

“…A task price is a significant potential factor that influences public participation. Therefore, a random forest algorithm-based task pricing model and task-accomplished model are computed based on the task attributes and neighboring-workers attributes. …”
Get full text

Leaf carbon nitrogen and phosphorus concentrations in dominant trees across China’s forests from 2005 to 2020 by Chenxi Li, Honglin He, Xiaoli Ren, Qian Xu, Shiyu Dong, Zining Lin, Luxiang Lin, Zexin Fan, Yongbiao Lin, Juxiu Liu, Qingkui Wang, Anzhi Wang, Ruiying Chang, Zongqiang Xie, Lingli Liu, Fusun Shi

“…Here we compiled and publicly released the Leaf Carbon-Nitrogen-Phosphorus Concentrations in China’s Forests (CNP−China) dataset, containing 628 standardized records from 52 dominant tree species across 11 representative China’s forest ecosystems from 2005 to 2020. …”
Get full text

An Improved Sparse Bayesian Learning SAR Tomography Method and its Application for Forest Vertical Structure Inversion by Jie Wan, Changcheng Wang, Peng Shen, Yonghui Wei

“…The results demonstrate that the proposed method achieved high-resolution SAR tomography imaging outcomes even within a limited baseline span. In terms of forest structure parameter inversion, the root mean square error (RMSE) of inverted forest height is 2.58 and 4.16 m compared to LiDAR measurements, while the RMSE of inverted underlying topography is 1.77 and 5.49 m. …”
Get full text

Impurity rates detection for pepper harvesting based on YOLOv8n-Seg-ASB and random forest by Lijian Lu, Jin Lei, Chenming Cheng, Shiguo Wang, Chengfu Wang, Xinyan Qin

“…Next, segmented principal component analysis (Seg-PCA) is employed to extract the fitting length and width of the segmentation mask contour. Finally, a random forest (RF) model is constructed to predict impurity rates by incorporating features such as mask pixel area, fitting length, fitting width and mask perimeter. …”
Get full text

General Framework for Georeferencing and Interpretation of Multi-Resolution LiDAR Data for Fine-Scale Forest Inventory by H. Hanafy, S.-Y. Shin, A. M. Eissa, Y. Hany, S. Park, S. Fei, A. Habib

“…Accurate forest inventory is critical for sustainable management, ecological assessment, and biomass estimation. …”
Get full text

Evaluation of potential productivity in coniferous forests by integrating field data and aerial laser scanning in Hidalgo, México by Rodrigo Ramos-Madrigal, Héctor M. de los Santos-Posadas, José René Valdez-Lazalde, Efraín Velasco-Bautista, Gregorio Ángeles-Pérez, Alma Delia Ortiz-Reyes

“… Aim of study: To predict the productivity potential of a managed conifer forest by estimating the site index from Light Detection and Ranging (LiDAR) data. …”
Get full text

Validation of the vertical canopy cover profile products derived from GEDI over selected forest sites by Yu Li, Hongliang Fang, Yao Wang, Sijia Li, Tian Ma, Yunjia Wu, Hao Tang

“…Compared with the ALS-estimated CC, needleleaf forest shows the highest correlation for vertical CC (r2 ≥ 0.65) and shrubland shows the lowest bias for total CC (bias = −0.13). …”
Get full text

Modeling pine forest growing stock volume in subtropical regions of China using airborne Lidar data by Zige Lan, Xiandie Jiang, Guiying Li, Yagang Lu, Hongwen Yao, Dengsheng Lu

“…The results indicate that: (1) HBA(Site), which models different pine forest types (i.e. pure pine forest (PPF) and mixed pine forest (MXF)) separately, with typical site as a stratification factor, provided the best estimation results with coefficient of determination (R2) of 0.80 and 0.74, root mean square error (RMSE) of 25.15 m3/ha and 23.86 m3/ha for PPF and MXF, respectively. …”
Get full text